TOOL WEAR PREDICTION BY DEEP LEARNING FROM AUGMENTABLE VISIBILITY GRAPH REPRESENTATION OF TIME SERIES DATA

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dc.contributor.authorTurker, Ilker
dc.contributor.authorTan, Serhat Orkun
dc.contributor.authorKutluana, Gokhan
dc.date.accessioned2024-09-29T16:12:25Z
dc.date.available2024-09-29T16:12:25Z
dc.date.issued2023
dc.departmentKarabük Üniversitesien_US
dc.description.abstractTool wear prediction has a crucial role for improving manufacturing quality and reliability due to optimizing tool replacement schedules, reducing downtime, and improving overall production efficiency. Deep learning models, having the ability to analyze large and complex datasets, can extract relevant information, and make accurate predictions about the condition of cutting tools. We propose a smart detection methodology based on converting the available sensory data collected from a CNC milling machine into a visibility graph representation. Due to the high dimensionality of the data with 44 attributes related to machining, a multilayer visibility graph representation is achieved after this conversion procedure, resulting in a 44-layered 128x128 adjacency matrix formation. A novel data augmentation technique specifically applicable to graph representation is also employed to increase the data size originally composed of 18 experiments into 360, each one represented as a multilayer graph. Augmented graph representations are further input to a custom CNN deep learning architecture with a split of 70% train, 10% validation and 20% test instances. Results indicate that Augmented Graph-induced classification of CNC mill tool with custom CNN model (GA-CNN) yields full accuracy for detecting whether the tool is worn or not.en_US
dc.identifier.endpage566en_US
dc.identifier.issn1221-5872
dc.identifier.issn2393-2988
dc.identifier.issue5en_US
dc.identifier.startpage557en_US
dc.identifier.urihttps://hdl.handle.net/20.500.14619/8752
dc.identifier.volume66en_US
dc.identifier.wosWOS:001267255200039en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherTechnical Univ Cluj-Napoca, Fac Machine Building Dept Systems Engen_US
dc.relation.ispartofActa Technica Napocensis Series-Applied Mathematics Mechanics and Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectKey words: Tool wear predictionen_US
dc.subjecttime series classificationen_US
dc.subjectvisibility graphen_US
dc.subjectdeep learningen_US
dc.subjectdataen_US
dc.subjectaugmentationen_US
dc.subjectsmart manufacturingen_US
dc.subjectIndustry 4.0.en_US
dc.titleTOOL WEAR PREDICTION BY DEEP LEARNING FROM AUGMENTABLE VISIBILITY GRAPH REPRESENTATION OF TIME SERIES DATAen_US
dc.typeArticleen_US

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